Overview

Dataset statistics

Number of variables24
Number of observations3669
Missing cells21
Missing cells (%)< 0.1%
Duplicate rows1272
Duplicate rows (%)34.7%
Total size in memory688.1 KiB
Average record size in memory192.0 B

Variable types

Numeric20
Categorical4

Alerts

Dataset has 1272 (34.7%) duplicate rowsDuplicates
BILL_AMT1 is highly overall correlated with BILL_AMT2 and 10 other fieldsHigh correlation
BILL_AMT2 is highly overall correlated with BILL_AMT1 and 11 other fieldsHigh correlation
BILL_AMT3 is highly overall correlated with BILL_AMT1 and 14 other fieldsHigh correlation
BILL_AMT4 is highly overall correlated with BILL_AMT1 and 13 other fieldsHigh correlation
BILL_AMT5 is highly overall correlated with BILL_AMT1 and 14 other fieldsHigh correlation
BILL_AMT6 is highly overall correlated with BILL_AMT1 and 13 other fieldsHigh correlation
EDUCATION is highly overall correlated with MARRIAGE and 1 other fieldsHigh correlation
MARRIAGE is highly overall correlated with EDUCATION and 1 other fieldsHigh correlation
PAY_0 is highly overall correlated with PAY_2High correlation
PAY_2 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_3 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_4 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_5 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_6 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_AMT1 is highly overall correlated with BILL_AMT1 and 3 other fieldsHigh correlation
PAY_AMT2 is highly overall correlated with BILL_AMT3 and 7 other fieldsHigh correlation
PAY_AMT3 is highly overall correlated with BILL_AMT2 and 9 other fieldsHigh correlation
PAY_AMT4 is highly overall correlated with BILL_AMT3 and 7 other fieldsHigh correlation
PAY_AMT5 is highly overall correlated with BILL_AMT5 and 5 other fieldsHigh correlation
PAY_AMT6 is highly overall correlated with BILL_AMT3 and 7 other fieldsHigh correlation
SEX is highly overall correlated with EDUCATION and 1 other fieldsHigh correlation
MARRIAGE is highly imbalanced (52.2%)Imbalance
PAY_AMT3 is highly skewed (γ1 = 28.75821047)Skewed
PAY_0 has 1741 (47.5%) zerosZeros
PAY_2 has 1901 (51.8%) zerosZeros
PAY_3 has 1875 (51.1%) zerosZeros
PAY_4 has 1995 (54.4%) zerosZeros
PAY_5 has 1996 (54.4%) zerosZeros
PAY_6 has 1879 (51.2%) zerosZeros
BILL_AMT1 has 244 (6.7%) zerosZeros
BILL_AMT2 has 328 (8.9%) zerosZeros
BILL_AMT3 has 384 (10.5%) zerosZeros
BILL_AMT4 has 424 (11.6%) zerosZeros
BILL_AMT5 has 460 (12.5%) zerosZeros
BILL_AMT6 has 532 (14.5%) zerosZeros
PAY_AMT1 has 667 (18.2%) zerosZeros
PAY_AMT2 has 708 (19.3%) zerosZeros
PAY_AMT3 has 798 (21.7%) zerosZeros
PAY_AMT4 has 808 (22.0%) zerosZeros
PAY_AMT5 has 827 (22.5%) zerosZeros
PAY_AMT6 has 949 (25.9%) zerosZeros

Reproduction

Analysis started2023-12-15 01:44:19.819936
Analysis finished2023-12-15 01:45:06.337529
Duration46.52 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ)

Distinct62
Distinct (%)1.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean166284.08
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:45:06.468451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3230000
95-th percentile420000
Maximum1000000
Range990000
Interquartile range (IQR)180000

Descriptive statistics

Standard deviation129512.16
Coefficient of variation (CV)0.77886083
Kurtosis1.0085552
Mean166284.08
Median Absolute Deviation (MAD)90000
Skewness1.0735486
Sum6.0993 × 108
Variance1.6773398 × 1010
MonotonicityNot monotonic
2023-12-14T19:45:06.653513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 453
 
12.3%
20000 236
 
6.4%
30000 191
 
5.2%
200000 182
 
5.0%
80000 165
 
4.5%
180000 135
 
3.7%
360000 122
 
3.3%
100000 118
 
3.2%
140000 117
 
3.2%
150000 115
 
3.1%
Other values (52) 1834
50.0%
ValueCountFrequency (%)
10000 53
 
1.4%
20000 236
6.4%
30000 191
5.2%
40000 30
 
0.8%
50000 453
12.3%
60000 98
 
2.7%
70000 91
 
2.5%
80000 165
 
4.5%
90000 96
 
2.6%
100000 118
 
3.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
700000 2
 
0.1%
680000 2
 
0.1%
670000 2
 
0.1%
630000 5
0.1%
620000 2
 
0.1%
610000 2
 
0.1%
600000 3
0.1%
580000 5
0.1%
550000 2
 
0.1%

SEX
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
female
2130 
male
1538 
SEX
 
1

Length

Max length6
Median length6
Mean length5.1608068
Min length3

Characters and Unicode

Total characters18935
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
female 2130
58.1%
male 1538
41.9%
SEX 1
 
< 0.1%

Length

2023-12-14T19:45:06.833167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T19:45:06.972098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
female 2130
58.1%
male 1538
41.9%
sex 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 5798
30.6%
m 3668
19.4%
a 3668
19.4%
l 3668
19.4%
f 2130
 
11.2%
S 1
 
< 0.1%
E 1
 
< 0.1%
X 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18932
> 99.9%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5798
30.6%
m 3668
19.4%
a 3668
19.4%
l 3668
19.4%
f 2130
 
11.3%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
E 1
33.3%
X 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 18935
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5798
30.6%
m 3668
19.4%
a 3668
19.4%
l 3668
19.4%
f 2130
 
11.2%
S 1
 
< 0.1%
E 1
 
< 0.1%
X 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5798
30.6%
m 3668
19.4%
a 3668
19.4%
l 3668
19.4%
f 2130
 
11.2%
S 1
 
< 0.1%
E 1
 
< 0.1%
X 1
 
< 0.1%

EDUCATION
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
university
1644 
graduate school
1401 
high school
596 
other
 
27
EDUCATION
 
1

Length

Max length15
Median length11
Mean length12.034614
Min length5

Characters and Unicode

Total characters44155
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowuniversity
2nd rowuniversity
3rd rowuniversity
4th rowuniversity
5th rowuniversity

Common Values

ValueCountFrequency (%)
university 1644
44.8%
graduate school 1401
38.2%
high school 596
 
16.2%
other 27
 
0.7%
EDUCATION 1
 
< 0.1%

Length

2023-12-14T19:45:07.123684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T19:45:07.251404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
school 1997
35.2%
university 1644
29.0%
graduate 1401
24.7%
high 596
 
10.5%
other 27
 
0.5%
education 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 4021
 
9.1%
i 3884
 
8.8%
s 3641
 
8.2%
h 3216
 
7.3%
e 3072
 
7.0%
r 3072
 
7.0%
t 3072
 
7.0%
u 3045
 
6.9%
a 2802
 
6.3%
1997
 
4.5%
Other values (16) 12333
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42149
95.5%
Space Separator 1997
 
4.5%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4021
 
9.5%
i 3884
 
9.2%
s 3641
 
8.6%
h 3216
 
7.6%
e 3072
 
7.3%
r 3072
 
7.3%
t 3072
 
7.3%
u 3045
 
7.2%
a 2802
 
6.6%
l 1997
 
4.7%
Other values (6) 10327
24.5%
Uppercase Letter
ValueCountFrequency (%)
E 1
11.1%
D 1
11.1%
U 1
11.1%
C 1
11.1%
A 1
11.1%
T 1
11.1%
I 1
11.1%
O 1
11.1%
N 1
11.1%
Space Separator
ValueCountFrequency (%)
1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42158
95.5%
Common 1997
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4021
 
9.5%
i 3884
 
9.2%
s 3641
 
8.6%
h 3216
 
7.6%
e 3072
 
7.3%
r 3072
 
7.3%
t 3072
 
7.3%
u 3045
 
7.2%
a 2802
 
6.6%
l 1997
 
4.7%
Other values (15) 10336
24.5%
Common
ValueCountFrequency (%)
1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4021
 
9.1%
i 3884
 
8.8%
s 3641
 
8.2%
h 3216
 
7.3%
e 3072
 
7.0%
r 3072
 
7.0%
t 3072
 
7.0%
u 3045
 
6.9%
a 2802
 
6.3%
1997
 
4.5%
Other values (16) 12333
27.9%

MARRIAGE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2
2045 
1
1559 
3
 
54
0
 
10
MARRIAGE
 
1

Length

Max length8
Median length1
Mean length1.0019079
Min length1

Characters and Unicode

Total characters3676
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 2045
55.7%
1 1559
42.5%
3 54
 
1.5%
0 10
 
0.3%
MARRIAGE 1
 
< 0.1%

Length

2023-12-14T19:45:07.426217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T19:45:07.576442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2045
55.7%
1 1559
42.5%
3 54
 
1.5%
0 10
 
0.3%
marriage 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 2045
55.6%
1 1559
42.4%
3 54
 
1.5%
0 10
 
0.3%
A 2
 
0.1%
R 2
 
0.1%
M 1
 
< 0.1%
I 1
 
< 0.1%
G 1
 
< 0.1%
E 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3668
99.8%
Uppercase Letter 8
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2
25.0%
R 2
25.0%
M 1
12.5%
I 1
12.5%
G 1
12.5%
E 1
12.5%
Decimal Number
ValueCountFrequency (%)
2 2045
55.8%
1 1559
42.5%
3 54
 
1.5%
0 10
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3668
99.8%
Latin 8
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2
25.0%
R 2
25.0%
M 1
12.5%
I 1
12.5%
G 1
12.5%
E 1
12.5%
Common
ValueCountFrequency (%)
2 2045
55.8%
1 1559
42.5%
3 54
 
1.5%
0 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2045
55.6%
1 1559
42.4%
3 54
 
1.5%
0 10
 
0.3%
A 2
 
0.1%
R 2
 
0.1%
M 1
 
< 0.1%
I 1
 
< 0.1%
G 1
 
< 0.1%
E 1
 
< 0.1%

AGE
Real number (ℝ)

Distinct52
Distinct (%)1.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.354144
Minimum21
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:45:07.742423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum75
Range54
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4381712
Coefficient of variation (CV)0.26696082
Kurtosis0.23723543
Mean35.354144
Median Absolute Deviation (MAD)6
Skewness0.80637489
Sum129679
Variance89.079075
MonotonicityNot monotonic
2023-12-14T19:45:07.951702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 214
 
5.8%
27 185
 
5.0%
30 174
 
4.7%
26 158
 
4.3%
24 155
 
4.2%
32 152
 
4.1%
34 151
 
4.1%
28 147
 
4.0%
31 145
 
4.0%
35 135
 
3.7%
Other values (42) 2052
55.9%
ValueCountFrequency (%)
21 7
 
0.2%
22 90
2.5%
23 124
3.4%
24 155
4.2%
25 131
3.6%
26 158
4.3%
27 185
5.0%
28 147
4.0%
29 214
5.8%
30 174
4.7%
ValueCountFrequency (%)
75 2
 
0.1%
73 2
 
0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
70 2
 
0.1%
67 5
0.1%
66 6
0.2%
65 2
 
0.1%
64 1
 
< 0.1%
63 4
0.1%

PAY_0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.00081788441
Minimum-2
Maximum8
Zeros1741
Zeros (%)47.5%
Negative1061
Negative (%)28.9%
Memory size28.8 KiB
2023-12-14T19:45:08.081141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.130073
Coefficient of variation (CV)1381.7026
Kurtosis6.1231377
Mean0.00081788441
Median Absolute Deviation (MAD)1
Skewness1.1940289
Sum3
Variance1.2770651
MonotonicityNot monotonic
2023-12-14T19:45:08.193634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1741
47.5%
-1 786
21.4%
1 495
 
13.5%
2 327
 
8.9%
-2 275
 
7.5%
3 25
 
0.7%
4 9
 
0.2%
8 9
 
0.2%
7 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 275
 
7.5%
-1 786
21.4%
0 1741
47.5%
1 495
 
13.5%
2 327
 
8.9%
3 25
 
0.7%
4 9
 
0.2%
7 1
 
< 0.1%
8 9
 
0.2%
ValueCountFrequency (%)
8 9
 
0.2%
7 1
 
< 0.1%
4 9
 
0.2%
3 25
 
0.7%
2 327
 
8.9%
1 495
 
13.5%
0 1741
47.5%
-1 786
21.4%
-2 275
 
7.5%

PAY_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.13631407
Minimum-2
Maximum7
Zeros1901
Zeros (%)51.8%
Negative1229
Negative (%)33.5%
Memory size28.8 KiB
2023-12-14T19:45:08.292199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2071782
Coefficient of variation (CV)-8.8558594
Kurtosis3.1275791
Mean-0.13631407
Median Absolute Deviation (MAD)0
Skewness1.0287184
Sum-500
Variance1.4572792
MonotonicityNot monotonic
2023-12-14T19:45:08.413911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1901
51.8%
-1 787
21.4%
2 487
 
13.3%
-2 442
 
12.0%
3 31
 
0.8%
7 9
 
0.2%
4 4
 
0.1%
1 3
 
0.1%
5 2
 
0.1%
6 2
 
0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 442
 
12.0%
-1 787
21.4%
0 1901
51.8%
1 3
 
0.1%
2 487
 
13.3%
3 31
 
0.8%
4 4
 
0.1%
5 2
 
0.1%
6 2
 
0.1%
7 9
 
0.2%
ValueCountFrequency (%)
7 9
 
0.2%
6 2
 
0.1%
5 2
 
0.1%
4 4
 
0.1%
3 31
 
0.8%
2 487
 
13.3%
1 3
 
0.1%
0 1901
51.8%
-1 787
21.4%
-2 442
 
12.0%

PAY_3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.1540349
Minimum-2
Maximum7
Zeros1875
Zeros (%)51.1%
Negative1272
Negative (%)34.7%
Memory size28.8 KiB
2023-12-14T19:45:08.515373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2546038
Coefficient of variation (CV)-8.1449321
Kurtosis4.1195494
Mean-0.1540349
Median Absolute Deviation (MAD)0
Skewness1.2639102
Sum-565
Variance1.5740306
MonotonicityNot monotonic
2023-12-14T19:45:08.645180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1875
51.1%
-1 797
21.7%
-2 475
 
12.9%
2 468
 
12.8%
4 14
 
0.4%
3 11
 
0.3%
7 10
 
0.3%
6 9
 
0.2%
5 6
 
0.2%
1 3
 
0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 475
 
12.9%
-1 797
21.7%
0 1875
51.1%
1 3
 
0.1%
2 468
 
12.8%
3 11
 
0.3%
4 14
 
0.4%
5 6
 
0.2%
6 9
 
0.2%
7 10
 
0.3%
ValueCountFrequency (%)
7 10
 
0.3%
6 9
 
0.2%
5 6
 
0.2%
4 14
 
0.4%
3 11
 
0.3%
2 468
 
12.8%
1 3
 
0.1%
0 1875
51.1%
-1 797
21.7%
-2 475
 
12.9%

PAY_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.25436205
Minimum-2
Maximum7
Zeros1995
Zeros (%)54.4%
Negative1291
Negative (%)35.2%
Memory size28.8 KiB
2023-12-14T19:45:08.756702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.183663
Coefficient of variation (CV)-4.6534576
Kurtosis4.7005083
Mean-0.25436205
Median Absolute Deviation (MAD)0
Skewness1.2605956
Sum-933
Variance1.4010581
MonotonicityNot monotonic
2023-12-14T19:45:08.865939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1995
54.4%
-1 753
 
20.5%
-2 538
 
14.7%
2 322
 
8.8%
3 29
 
0.8%
5 12
 
0.3%
4 9
 
0.2%
7 9
 
0.2%
6 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 538
 
14.7%
-1 753
 
20.5%
0 1995
54.4%
2 322
 
8.8%
3 29
 
0.8%
4 9
 
0.2%
5 12
 
0.3%
6 1
 
< 0.1%
7 9
 
0.2%
ValueCountFrequency (%)
7 9
 
0.2%
6 1
 
< 0.1%
5 12
 
0.3%
4 9
 
0.2%
3 29
 
0.8%
2 322
 
8.8%
0 1995
54.4%
-1 753
 
20.5%
-2 538
 
14.7%

PAY_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.2674482
Minimum-2
Maximum7
Zeros1996
Zeros (%)54.4%
Negative1296
Negative (%)35.3%
Memory size28.8 KiB
2023-12-14T19:45:08.967862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1649636
Coefficient of variation (CV)-4.3558477
Kurtosis4.6026359
Mean-0.2674482
Median Absolute Deviation (MAD)0
Skewness1.1873709
Sum-981
Variance1.3571403
MonotonicityNot monotonic
2023-12-14T19:45:09.062477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1996
54.4%
-1 749
 
20.4%
-2 547
 
14.9%
2 328
 
8.9%
3 18
 
0.5%
4 18
 
0.5%
7 10
 
0.3%
5 2
 
0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 547
 
14.9%
-1 749
 
20.4%
0 1996
54.4%
2 328
 
8.9%
3 18
 
0.5%
4 18
 
0.5%
5 2
 
0.1%
7 10
 
0.3%
ValueCountFrequency (%)
7 10
 
0.3%
5 2
 
0.1%
4 18
 
0.5%
3 18
 
0.5%
2 328
 
8.9%
0 1996
54.4%
-1 749
 
20.4%
-2 547
 
14.9%

PAY_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.29389313
Minimum-2
Maximum8
Zeros1879
Zeros (%)51.2%
Negative1393
Negative (%)38.0%
Memory size28.8 KiB
2023-12-14T19:45:09.173409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1900395
Coefficient of variation (CV)-4.0492252
Kurtosis4.3412627
Mean-0.29389313
Median Absolute Deviation (MAD)0
Skewness1.1879815
Sum-1078
Variance1.4161939
MonotonicityNot monotonic
2023-12-14T19:45:09.298487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1879
51.2%
-1 810
22.1%
-2 583
 
15.9%
2 347
 
9.5%
3 30
 
0.8%
6 8
 
0.2%
7 6
 
0.2%
4 4
 
0.1%
8 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 583
 
15.9%
-1 810
22.1%
0 1879
51.2%
2 347
 
9.5%
3 30
 
0.8%
4 4
 
0.1%
6 8
 
0.2%
7 6
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 6
 
0.2%
6 8
 
0.2%
4 4
 
0.1%
3 30
 
0.8%
2 347
 
9.5%
0 1879
51.2%
-1 810
22.1%
-2 583
 
15.9%

BILL_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2137
Distinct (%)58.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean51054.57
Minimum-14386
Maximum964511
Zeros244
Zeros (%)6.7%
Negative83
Negative (%)2.3%
Memory size28.8 KiB
2023-12-14T19:45:09.441958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-14386
5-th percentile0
Q13096
median21148
Q363638.75
95-th percentile203183
Maximum964511
Range978897
Interquartile range (IQR)60542.75

Descriptive statistics

Standard deviation76373.892
Coefficient of variation (CV)1.4959266
Kurtosis13.444545
Mean51054.57
Median Absolute Deviation (MAD)20735.5
Skewness2.9687536
Sum1.8726816 × 108
Variance5.8329714 × 109
MonotonicityNot monotonic
2023-12-14T19:45:09.632278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 244
 
6.7%
390 31
 
0.8%
780 12
 
0.3%
316 11
 
0.3%
396 10
 
0.3%
-200 8
 
0.2%
291 7
 
0.2%
2400 7
 
0.2%
1261 6
 
0.2%
326 6
 
0.2%
Other values (2127) 3326
90.7%
ValueCountFrequency (%)
-14386 2
0.1%
-2000 3
0.1%
-1886 1
 
< 0.1%
-1540 2
0.1%
-1312 2
0.1%
-1100 2
0.1%
-984 1
 
< 0.1%
-946 2
0.1%
-819 1
 
< 0.1%
-800 1
 
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
546741 1
< 0.1%
507726 2
0.1%
507062 2
0.1%
495559 1
< 0.1%
485921 1
< 0.1%
482250 1
< 0.1%
471814 2
0.1%
467150 2
0.1%
459600 1
< 0.1%

BILL_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2091
Distinct (%)57.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean49166.293
Minimum-24704
Maximum983931
Zeros328
Zeros (%)8.9%
Negative92
Negative (%)2.5%
Memory size28.8 KiB
2023-12-14T19:45:09.822505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-24704
5-th percentile0
Q13123
median20417
Q361162
95-th percentile196143
Maximum983931
Range1008635
Interquartile range (IQR)58039

Descriptive statistics

Standard deviation74703.121
Coefficient of variation (CV)1.5193971
Kurtosis15.094803
Mean49166.293
Median Absolute Deviation (MAD)20089
Skewness3.1001486
Sum1.8034196 × 108
Variance5.5805563 × 109
MonotonicityNot monotonic
2023-12-14T19:45:10.023551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 328
 
8.9%
390 17
 
0.5%
-200 14
 
0.4%
316 13
 
0.4%
291 10
 
0.3%
780 9
 
0.2%
326 8
 
0.2%
300 8
 
0.2%
396 7
 
0.2%
2400 7
 
0.2%
Other values (2081) 3247
88.5%
ValueCountFrequency (%)
-24704 1
< 0.1%
-13543 2
0.1%
-9850 2
0.1%
-2760 1
< 0.1%
-2685 1
< 0.1%
-2479 1
< 0.1%
-2086 1
< 0.1%
-2000 1
< 0.1%
-1930 2
0.1%
-1100 2
0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
535509 1
< 0.1%
509229 2
0.1%
506260 2
0.1%
491956 2
0.1%
478380 2
0.1%
475931 1
< 0.1%
470915 1
< 0.1%
458862 2
0.1%
450047 1
< 0.1%

BILL_AMT3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2044
Distinct (%)55.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean45602.332
Minimum-9850
Maximum548020
Zeros384
Zeros (%)10.5%
Negative82
Negative (%)2.2%
Memory size28.8 KiB
2023-12-14T19:45:10.199022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9850
5-th percentile0
Q12252
median19505
Q356272.75
95-th percentile186452.55
Maximum548020
Range557870
Interquartile range (IQR)54020.75

Descriptive statistics

Standard deviation69402.889
Coefficient of variation (CV)1.5219153
Kurtosis10.55125
Mean45602.332
Median Absolute Deviation (MAD)19115
Skewness2.8652667
Sum1.6726935 × 108
Variance4.8167609 × 109
MonotonicityNot monotonic
2023-12-14T19:45:10.382268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
 
10.5%
390 31
 
0.8%
780 11
 
0.3%
-2 9
 
0.2%
291 9
 
0.2%
316 8
 
0.2%
396 7
 
0.2%
2400 7
 
0.2%
200 6
 
0.2%
-200 6
 
0.2%
Other values (2034) 3190
86.9%
ValueCountFrequency (%)
-9850 2
0.1%
-6144 1
< 0.1%
-3650 1
< 0.1%
-2697 2
0.1%
-2643 1
< 0.1%
-2320 2
0.1%
-2000 1
< 0.1%
-1690 2
0.1%
-1117 1
< 0.1%
-946 2
0.1%
ValueCountFrequency (%)
548020 1
< 0.1%
535020 1
< 0.1%
499936 2
0.1%
479432 2
0.1%
471175 1
< 0.1%
469703 2
0.1%
460317 1
< 0.1%
455286 1
< 0.1%
453770 1
< 0.1%
445129 1
< 0.1%

BILL_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2008
Distinct (%)54.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41468.668
Minimum-7905
Maximum891586
Zeros424
Zeros (%)11.6%
Negative84
Negative (%)2.3%
Memory size28.8 KiB
2023-12-14T19:45:10.578578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7905
5-th percentile0
Q11741.5
median17915
Q349324
95-th percentile168644.4
Maximum891586
Range899491
Interquartile range (IQR)47582.5

Descriptive statistics

Standard deviation67774.637
Coefficient of variation (CV)1.6343577
Kurtosis20.350785
Mean41468.668
Median Absolute Deviation (MAD)17475.5
Skewness3.6105903
Sum1.5210707 × 108
Variance4.5934014 × 109
MonotonicityNot monotonic
2023-12-14T19:45:10.927237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 424
 
11.6%
390 25
 
0.7%
316 15
 
0.4%
291 10
 
0.3%
326 9
 
0.2%
300 8
 
0.2%
2400 7
 
0.2%
416 7
 
0.2%
780 7
 
0.2%
-2 6
 
0.2%
Other values (1998) 3150
85.9%
ValueCountFrequency (%)
-7905 1
< 0.1%
-3684 2
0.1%
-3650 1
< 0.1%
-3450 1
< 0.1%
-2898 2
0.1%
-2618 2
0.1%
-2054 1
< 0.1%
-2000 1
< 0.1%
-1513 1
< 0.1%
-1400 2
0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
628699 2
0.1%
542653 2
0.1%
530672 1
< 0.1%
505507 2
0.1%
487066 2
0.1%
486776 1
< 0.1%
479978 2
0.1%
472621 1
< 0.1%
452162 1
< 0.1%

BILL_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1983
Distinct (%)54.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean39896.161
Minimum-28335
Maximum927171
Zeros460
Zeros (%)12.5%
Negative87
Negative (%)2.4%
Memory size28.8 KiB
2023-12-14T19:45:11.156036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-28335
5-th percentile0
Q11480.5
median17616
Q348449
95-th percentile168329
Maximum927171
Range955506
Interquartile range (IQR)46968.5

Descriptive statistics

Standard deviation63815.839
Coefficient of variation (CV)1.5995484
Kurtosis20.247809
Mean39896.161
Median Absolute Deviation (MAD)17220
Skewness3.4404962
Sum1.4633912 × 108
Variance4.0724613 × 109
MonotonicityNot monotonic
2023-12-14T19:45:11.393759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 460
 
12.5%
390 28
 
0.8%
316 11
 
0.3%
396 11
 
0.3%
150 8
 
0.2%
2400 7
 
0.2%
2000 7
 
0.2%
780 7
 
0.2%
416 7
 
0.2%
1261 6
 
0.2%
Other values (1973) 3116
84.9%
ValueCountFrequency (%)
-28335 2
0.1%
-10213 1
< 0.1%
-5000 2
0.1%
-3876 1
< 0.1%
-3650 1
< 0.1%
-3450 1
< 0.1%
-3272 2
0.1%
-2946 1
< 0.1%
-2153 1
< 0.1%
-2000 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
503914 1
< 0.1%
484993 1
< 0.1%
484612 2
0.1%
483003 2
0.1%
471145 2
0.1%
440982 2
0.1%
392879 1
< 0.1%
392650 1
< 0.1%
377858 1
< 0.1%

BILL_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1947
Distinct (%)53.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean38776.507
Minimum-339603
Maximum961664
Zeros532
Zeros (%)14.5%
Negative71
Negative (%)1.9%
Memory size28.8 KiB
2023-12-14T19:45:11.603486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q1981.5
median15837.5
Q347550
95-th percentile168108.45
Maximum961664
Range1301267
Interquartile range (IQR)46568.5

Descriptive statistics

Standard deviation64744.765
Coefficient of variation (CV)1.6696905
Kurtosis23.443315
Mean38776.507
Median Absolute Deviation (MAD)15638
Skewness3.4521135
Sum1.4223223 × 108
Variance4.1918845 × 109
MonotonicityNot monotonic
2023-12-14T19:45:11.830476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 532
 
14.5%
390 22
 
0.6%
780 16
 
0.4%
150 13
 
0.4%
316 12
 
0.3%
291 11
 
0.3%
326 11
 
0.3%
396 7
 
0.2%
2500 6
 
0.2%
-2 6
 
0.2%
Other values (1937) 3032
82.6%
ValueCountFrequency (%)
-339603 2
0.1%
-16586 1
< 0.1%
-11060 1
< 0.1%
-4306 1
< 0.1%
-3650 1
< 0.1%
-3614 1
< 0.1%
-3272 2
0.1%
-2946 1
< 0.1%
-2389 1
< 0.1%
-2303 2
0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
527711 1
< 0.1%
496915 1
< 0.1%
473944 2
0.1%
469961 2
0.1%
434715 2
0.1%
419643 2
0.1%
398478 1
< 0.1%
391336 1
< 0.1%

PAY_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1146
Distinct (%)31.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5573.8424
Minimum0
Maximum239104
Zeros667
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:45:12.034896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2145
Q35006
95-th percentile20000
Maximum239104
Range239104
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation13736.634
Coefficient of variation (CV)2.464482
Kurtosis103.26738
Mean5573.8424
Median Absolute Deviation (MAD)1946
Skewness8.5411615
Sum20444854
Variance1.8869512 × 108
MonotonicityNot monotonic
2023-12-14T19:45:12.283801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 667
 
18.2%
2000 147
 
4.0%
3000 102
 
2.8%
5000 76
 
2.1%
10000 64
 
1.7%
1000 62
 
1.7%
2500 60
 
1.6%
1500 55
 
1.5%
4000 47
 
1.3%
1600 30
 
0.8%
Other values (1136) 2358
64.3%
ValueCountFrequency (%)
0 667
18.2%
1 2
 
0.1%
5 2
 
0.1%
13 2
 
0.1%
20 2
 
0.1%
23 1
 
< 0.1%
27 1
 
< 0.1%
39 4
 
0.1%
44 1
 
< 0.1%
92 2
 
0.1%
ValueCountFrequency (%)
239104 2
0.1%
210000 1
< 0.1%
199646 2
0.1%
163500 1
< 0.1%
160444 1
< 0.1%
140013 1
< 0.1%
120093 2
0.1%
120041 2
0.1%
100000 2
0.1%
90000 2
0.1%

PAY_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1129
Distinct (%)30.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5131.4654
Minimum0
Maximum285138
Zeros708
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:45:12.510576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1495
median1972.5
Q34913
95-th percentile18112
Maximum285138
Range285138
Interquartile range (IQR)4418

Descriptive statistics

Standard deviation14581.833
Coefficient of variation (CV)2.8416508
Kurtosis134.73837
Mean5131.4654
Median Absolute Deviation (MAD)1943.5
Skewness9.9940173
Sum18822215
Variance2.1262984 × 108
MonotonicityNot monotonic
2023-12-14T19:45:12.748556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 708
 
19.3%
2000 146
 
4.0%
5000 95
 
2.6%
1000 95
 
2.6%
3000 94
 
2.6%
1500 89
 
2.4%
1200 39
 
1.1%
4000 38
 
1.0%
1400 34
 
0.9%
390 34
 
0.9%
Other values (1119) 2296
62.6%
ValueCountFrequency (%)
0 708
19.3%
1 4
 
0.1%
2 4
 
0.1%
3 2
 
0.1%
5 4
 
0.1%
7 3
 
0.1%
8 1
 
< 0.1%
10 2
 
0.1%
11 2
 
0.1%
12 2
 
0.1%
ValueCountFrequency (%)
285138 2
0.1%
199982 2
0.1%
182123 1
< 0.1%
180519 1
< 0.1%
177671 2
0.1%
170000 1
< 0.1%
167622 1
< 0.1%
145000 2
0.1%
129990 1
< 0.1%
121715 1
< 0.1%

PAY_AMT3
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1040
Distinct (%)28.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4723.5365
Minimum0
Maximum896040
Zeros798
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:45:12.978046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1240
median1394.5
Q34000
95-th percentile15310.3
Maximum896040
Range896040
Interquartile range (IQR)3760

Descriptive statistics

Standard deviation19414.123
Coefficient of variation (CV)4.1100822
Kurtosis1228.6441
Mean4723.5365
Median Absolute Deviation (MAD)1394.5
Skewness28.75821
Sum17325932
Variance3.7690819 × 108
MonotonicityNot monotonic
2023-12-14T19:45:13.130897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 798
 
21.7%
1000 172
 
4.7%
2000 152
 
4.1%
3000 100
 
2.7%
5000 82
 
2.2%
1500 55
 
1.5%
4000 48
 
1.3%
10000 44
 
1.2%
6000 29
 
0.8%
2500 29
 
0.8%
Other values (1030) 2159
58.8%
ValueCountFrequency (%)
0 798
21.7%
2 2
 
0.1%
3 2
 
0.1%
4 2
 
0.1%
5 3
 
0.1%
6 1
 
< 0.1%
9 3
 
0.1%
10 3
 
0.1%
12 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
222750 2
0.1%
155000 1
< 0.1%
153400 1
< 0.1%
152618 2
0.1%
148307 1
< 0.1%
133657 2
0.1%
130000 2
0.1%
116446 1
< 0.1%
110699 1
< 0.1%

PAY_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1034
Distinct (%)28.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4828.5698
Minimum0
Maximum205000
Zeros808
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:45:13.264822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1231.5
median1500
Q34000
95-th percentile17024
Maximum205000
Range205000
Interquartile range (IQR)3768.5

Descriptive statistics

Standard deviation13764.722
Coefficient of variation (CV)2.850683
Kurtosis72.434939
Mean4828.5698
Median Absolute Deviation (MAD)1500
Skewness7.5169861
Sum17711194
Variance1.8946757 × 108
MonotonicityNot monotonic
2023-12-14T19:45:13.403552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 808
 
22.0%
1000 160
 
4.4%
2000 142
 
3.9%
3000 94
 
2.6%
5000 91
 
2.5%
1500 71
 
1.9%
4000 49
 
1.3%
500 41
 
1.1%
2500 37
 
1.0%
10000 36
 
1.0%
Other values (1024) 2139
58.3%
ValueCountFrequency (%)
0 808
22.0%
2 5
 
0.1%
3 1
 
< 0.1%
4 2
 
0.1%
6 6
 
0.2%
7 2
 
0.1%
8 1
 
< 0.1%
10 2
 
0.1%
17 2
 
0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
205000 1
 
< 0.1%
188840 2
0.1%
178460 1
 
< 0.1%
171716 1
 
< 0.1%
161110 1
 
< 0.1%
159212 1
 
< 0.1%
146900 2
0.1%
125009 1
 
< 0.1%
107591 2
0.1%
100000 4
0.1%

PAY_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1038
Distinct (%)28.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5093.6881
Minimum0
Maximum332000
Zeros827
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:45:13.531181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1214.25
median1500
Q34000
95-th percentile16017.55
Maximum332000
Range332000
Interquartile range (IQR)3785.75

Descriptive statistics

Standard deviation17400.958
Coefficient of variation (CV)3.4161805
Kurtosis137.482
Mean5093.6881
Median Absolute Deviation (MAD)1500
Skewness10.115542
Sum18683648
Variance3.0279335 × 108
MonotonicityNot monotonic
2023-12-14T19:45:13.667564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 827
 
22.5%
1000 163
 
4.4%
2000 123
 
3.4%
3000 119
 
3.2%
5000 77
 
2.1%
1500 73
 
2.0%
4000 48
 
1.3%
2500 33
 
0.9%
500 28
 
0.8%
3500 27
 
0.7%
Other values (1028) 2150
58.6%
ValueCountFrequency (%)
0 827
22.5%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
12 3
 
0.1%
20 2
 
0.1%
24 1
 
< 0.1%
32 2
 
0.1%
ValueCountFrequency (%)
332000 2
0.1%
326889 1
< 0.1%
200000 1
< 0.1%
195599 2
0.1%
184922 2
0.1%
162000 2
0.1%
161000 2
0.1%
160719 2
0.1%
158064 1
< 0.1%
145564 1
< 0.1%

PAY_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct970
Distinct (%)26.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5188.7181
Minimum0
Maximum528666
Zeros949
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:45:13.814202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1338
Q34000
95-th percentile15709.95
Maximum528666
Range528666
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation20925.17
Coefficient of variation (CV)4.0328207
Kurtosis270.44793
Mean5188.7181
Median Absolute Deviation (MAD)1338
Skewness13.813886
Sum19032218
Variance4.3786272 × 108
MonotonicityNot monotonic
2023-12-14T19:45:13.994096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 949
25.9%
1000 176
 
4.8%
2000 163
 
4.4%
5000 96
 
2.6%
3000 94
 
2.6%
1500 62
 
1.7%
4000 57
 
1.6%
10000 39
 
1.1%
2500 37
 
1.0%
6000 28
 
0.8%
Other values (960) 1967
53.6%
ValueCountFrequency (%)
0 949
25.9%
1 3
 
0.1%
3 3
 
0.1%
4 2
 
0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
12 2
 
0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
528666 2
0.1%
345293 2
0.1%
223833 1
< 0.1%
208896 1
< 0.1%
185652 2
0.1%
175000 1
< 0.1%
173869 1
< 0.1%
171944 2
0.1%
167000 2
0.1%
159753 1
< 0.1%
Distinct2
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size28.8 KiB
1.0
2873 
0.0
795 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11004
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2873
78.3%
0.0 795
 
21.7%
(Missing) 1
 
< 0.1%

Length

2023-12-14T19:45:14.124982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T19:45:14.216984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2873
78.3%
0.0 795
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0 4463
40.6%
. 3668
33.3%
1 2873
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7336
66.7%
Other Punctuation 3668
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4463
60.8%
1 2873
39.2%
Other Punctuation
ValueCountFrequency (%)
. 3668
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4463
40.6%
. 3668
33.3%
1 2873
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4463
40.6%
. 3668
33.3%
1 2873
26.1%

Interactions

2023-12-14T19:45:03.259004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:21.597465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:24.072374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:26.229692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:28.103153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:29.986982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:31.993405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:33.823146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:35.859086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:38.165718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:40.992621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:43.113549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:45.088913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:47.179520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:49.034593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:51.401369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.439394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:56.242409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.465012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:00.651631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:03.387733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:21.755762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:24.260394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:26.333875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:28.191518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:30.072212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:32.079583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:33.927253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:35.972731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:38.294411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:41.099377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:43.219255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:45.177643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:47.274400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:49.147775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:51.496122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.574533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:56.373855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.560062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:00.840832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:03.493705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:21.875504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:24.374854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:26.421977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:28.277722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:30.156541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:32.170004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:34.034033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:36.072663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:38.417758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:41.221644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:43.316625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:45.285024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:47.361779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:49.245522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:51.601325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.697049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:56.499087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.659067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:00.954715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:03.605715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:22.010956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:24.473318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:26.506046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:28.360055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:30.238960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:32.255131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:34.121098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:36.166986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:38.542328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:41.330459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:43.405482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:45.385046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:47.460994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:49.338199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:51.713275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.813512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:56.685782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.760158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:01.058398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:03.695259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:22.131030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:24.556330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:26.595403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:28.444099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:30.323650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:32.336813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:34.200321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:36.300250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:38.671154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:41.426127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:43.493764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:45.527563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:47.548721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:49.436188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:51.836072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.927667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:56.867548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.852004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:01.156193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:03.797697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:22.247012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:24.638508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:26.691866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:28.535599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:30.406384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:32.418440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:34.281727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:36.404209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:38.829552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:41.509873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-14T19:44:44.638707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:46.741743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:48.590380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:50.856248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:52.979250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:55.630043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:57.951619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:00.111625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:02.582199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:04.891160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:23.593613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:25.868216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:27.741821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:29.639121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:31.526785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:33.446559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:35.454005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:37.605477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:40.417918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:42.754004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:44.737234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:46.831762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:48.676453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:50.975738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.064037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:55.750351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.058616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:00.215332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:02.687290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:04.976526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:23.700438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:25.961655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:27.838795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:29.720372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:31.673680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:33.543478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:35.544188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:37.742982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:40.542436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:42.842166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:44.820451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:46.921161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:48.765810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:51.080026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.154450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:55.873733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.148020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:00.310735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:02.795840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:05.060728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:23.815765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:26.048035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:27.924818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:29.803909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:31.792512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:33.633759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:35.632358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:37.874371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:40.718674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:42.928142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:44.903797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:47.001368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:48.855385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:51.182157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.245883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:55.987090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.239077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:00.412666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:02.939799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:05.161582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:23.940775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:26.137186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:28.015440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:29.891920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:31.905670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:33.736413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:35.765510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:38.017228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:40.853220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:43.022184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:44.994950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:47.094942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:48.946960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:51.297251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:53.342117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:56.107475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:44:58.363632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:00.523906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-14T19:45:03.094939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-14T19:45:14.364159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AGEBILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6EDUCATIONLIMIT_BALMARRIAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6SEXdefault payment next month
AGE1.0000.0110.0100.0150.0110.0080.0080.1840.2180.307-0.043-0.072-0.076-0.062-0.056-0.0530.0460.0810.0430.0150.0410.0510.1070.077
BILL_AMT10.0111.0000.9130.8600.8150.7790.7540.0350.0750.0200.2760.5830.5420.5310.5150.5160.5170.4770.4880.4550.4380.4410.0730.032
BILL_AMT20.0100.9131.0000.9010.8480.8020.7780.0410.0870.0000.2960.5670.6000.5630.5400.5400.6510.4910.5060.4660.4540.4660.0820.024
BILL_AMT30.0150.8600.9011.0000.9080.8560.8210.0760.0950.0000.2870.5350.5770.6460.6070.5930.5420.6370.5290.5000.4910.5040.0570.036
BILL_AMT40.0110.8150.8480.9081.0000.8970.8450.0370.0920.0250.2860.5340.5620.6390.6690.6300.4980.5640.6500.5080.4940.5160.0380.036
BILL_AMT50.0080.7790.8020.8560.8971.0000.8910.0260.0900.0390.2830.5070.5390.6010.6300.6830.4660.5250.5560.6620.5040.5410.0440.032
BILL_AMT60.0080.7540.7780.8210.8450.8911.0000.0420.0920.0100.2910.4880.5180.5730.5870.6520.4520.5030.5250.5750.6690.5450.0580.058
EDUCATION0.1840.0350.0410.0760.0370.0260.0421.000-0.1940.5120.1200.1980.1780.1670.1410.160-0.007-0.020-0.0260.0240.006-0.0050.7080.060
LIMIT_BAL0.2180.0750.0870.0950.0920.0900.092-0.1941.0000.094-0.209-0.295-0.286-0.271-0.269-0.2620.2960.2720.2820.2470.3000.3200.0900.062
MARRIAGE0.3070.0200.0000.0000.0250.0390.0100.5120.0941.0000.0360.0490.0460.0570.0530.052-0.039-0.038-0.037-0.015-0.049-0.0420.7080.037
PAY_0-0.0430.2760.2960.2870.2860.2830.2910.120-0.2090.0361.0000.5610.4980.4460.4250.416-0.105-0.062-0.039-0.012-0.020-0.0240.0540.376
PAY_2-0.0720.5830.5670.5350.5340.5070.4880.198-0.2950.0490.5611.0000.7980.7050.6910.6620.0480.1080.1440.1230.1030.1310.0630.276
PAY_3-0.0760.5420.6000.5770.5620.5390.5180.178-0.2860.0460.4980.7981.0000.8080.7290.6910.2500.0630.1420.1560.1470.1520.0640.263
PAY_4-0.0620.5310.5630.6460.6390.6010.5730.167-0.2710.0570.4460.7050.8081.0000.8340.7540.1890.2690.1250.1800.1760.2050.0580.225
PAY_5-0.0560.5150.5400.6070.6690.6300.5870.141-0.2690.0530.4250.6910.7290.8341.0000.8200.1640.2460.2940.1260.1920.2160.0620.253
PAY_6-0.0530.5160.5400.5930.6300.6830.6520.160-0.2620.0520.4160.6620.6910.7540.8201.0000.1660.2320.2600.3170.1600.2390.0730.183
PAY_AMT10.0460.5170.6510.5420.4980.4660.452-0.0070.296-0.039-0.1050.0480.2500.1890.1640.1661.0000.4710.5120.4530.4710.4550.0000.062
PAY_AMT20.0810.4770.4910.6370.5640.5250.503-0.0200.272-0.038-0.0620.1080.0630.2690.2460.2320.4711.0000.5340.5290.5020.5080.0290.054
PAY_AMT30.0430.4880.5060.5290.6500.5560.525-0.0260.282-0.037-0.0390.1440.1420.1250.2940.2600.5120.5341.0000.5100.5170.5190.0280.000
PAY_AMT40.0150.4550.4660.5000.5080.6620.5750.0240.247-0.015-0.0120.1230.1560.1800.1260.3170.4530.5290.5101.0000.5130.5450.0210.032
PAY_AMT50.0410.4380.4540.4910.4940.5040.6690.0060.300-0.049-0.0200.1030.1470.1760.1920.1600.4710.5020.5170.5131.0000.5430.0700.037
PAY_AMT60.0510.4410.4660.5040.5160.5410.545-0.0050.320-0.042-0.0240.1310.1520.2050.2160.2390.4550.5080.5190.5450.5431.0000.0290.041
SEX0.1070.0730.0820.0570.0380.0440.0580.7080.0900.7080.0540.0630.0640.0580.0620.0730.0000.0290.0280.0210.0700.0291.0000.000
default payment next month0.0770.0320.0240.0360.0360.0320.0580.0600.0620.0370.3760.2760.2630.2250.2530.1830.0620.0540.0000.0320.0370.0410.0001.000

Missing values

2023-12-14T19:45:05.371816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-14T19:45:05.701971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-14T19:45:06.038811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
120000.0femaleuniversity124.02.02.0-1.0-1.0-2.0-2.03913.03102.0689.00.00.00.00.0689.00.00.00.00.00.0
2120000.0femaleuniversity226.0-1.02.00.00.00.02.02682.01725.02682.03272.03455.03261.00.01000.01000.01000.00.02000.00.0
390000.0femaleuniversity234.00.00.00.00.00.00.029239.014027.013559.014331.014948.015549.01518.01500.01000.01000.01000.05000.01.0
450000.0femaleuniversity137.00.00.00.00.00.00.046990.048233.049291.028314.028959.029547.02000.02019.01200.01100.01069.01000.01.0
550000.0maleuniversity157.0-1.00.0-1.00.00.00.08617.05670.035835.020940.019146.019131.02000.036681.010000.09000.0689.0679.01.0
650000.0malegraduate school237.00.00.00.00.00.00.064400.057069.057608.019394.019619.020024.02500.01815.0657.01000.01000.0800.01.0
7500000.0malegraduate school229.00.00.00.00.00.00.0367965.0412023.0445007.0542653.0483003.0473944.055000.040000.038000.020239.013750.013770.01.0
8100000.0femaleuniversity223.00.0-1.0-1.00.00.0-1.011876.0380.0601.0221.0-159.0567.0380.0601.00.0581.01687.01542.01.0
9140000.0femalehigh school128.00.00.02.00.00.00.011285.014096.012108.012211.011793.03719.03329.00.0432.01000.01000.01000.01.0
1020000.0malehigh school235.0-2.0-2.0-2.0-2.0-1.0-1.00.00.00.00.013007.013912.00.00.00.013007.01122.00.01.0
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
3660380000.0maleuniversity150.00.00.00.00.00.00.0385662.0294826.0220022.0154283.035270.0332270.012020.09009.06109.03000.0332000.012000.00.0
366150000.0maleuniversity144.00.00.00.00.00.00.045335.046027.030286.026275.026823.027371.01524.01427.0941.0972.0992.01000.01.0
3662150000.0femalehigh school143.0-1.0-1.02.00.0-1.0-1.0264.0948.0632.0316.0316.01414.01000.00.00.0316.01414.00.00.0
3663220000.0maleuniversity229.00.00.00.00.00.00.0122286.0122839.0123035.0114385.0115903.0118528.05008.05007.06007.05000.04700.05503.01.0
366480000.0femaleother227.00.00.00.00.00.00.045268.047140.047411.048443.049478.043264.02600.01800.01700.01700.01700.01300.01.0
3665220000.0femaleuniversity132.00.00.00.00.00.00.0194961.0197536.0203251.0208355.0213015.0217475.07200.09000.010000.08000.08010.08500.01.0
366670000.0femaleuniversity234.01.02.02.02.00.00.024208.025015.027189.026456.028361.031873.01500.02900.00.02500.04000.00.01.0
3667120000.0maleuniversity237.0-1.02.00.00.00.02.016241.016680.017695.017901.019608.019143.01000.01600.0800.02000.00.01600.00.0
3668180000.0femaleuniversity232.00.00.00.00.00.00.020730.017107.035884.031057.029052.025933.01582.030000.01000.01000.01000.01000.01.0
366950000.0femalehigh school157.00.00.00.00.00.00.049017.050690.047487.048319.048449.049656.02500.02000.02000.01746.02000.01800.01.0

Duplicate rows

Most frequently occurring

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month# duplicates
010000.0femalehigh school222.00.00.00.00.0-2.0-2.08109.09778.08259.00.00.00.02000.01036.00.00.00.00.00.02
110000.0femaleuniversity131.00.00.00.00.00.00.015915.09050.09901.09975.09736.08703.02330.02200.01000.0333.0311.0322.01.02
210000.0femaleuniversity222.01.02.00.00.00.00.010250.08558.010525.010050.09903.09984.00.02126.0390.0328.0476.01287.01.02
310000.0malehigh school223.00.00.00.00.00.02.06974.07838.09002.09182.09729.09411.01134.01298.0478.0847.00.0175.01.02
410000.0malehigh school235.00.00.00.00.00.00.07877.08918.09864.09673.09414.09156.01174.01120.0310.0316.01000.02000.01.02
510000.0maleuniversity132.01.02.02.02.02.02.08425.08148.09481.09180.010052.010091.00.01632.00.01022.0350.00.01.02
610000.0maleuniversity145.00.00.00.02.00.00.07139.08416.09815.09508.09754.010192.01400.01700.00.0400.0600.0200.00.02
710000.0maleuniversity156.02.02.02.00.00.00.02097.04193.03978.04062.04196.04326.02300.00.0150.0200.0200.0160.00.02
810000.0maleuniversity222.00.00.00.00.00.00.01877.03184.06003.03576.03670.04451.01500.02927.01000.0300.01000.0500.01.02
910000.0maleuniversity222.00.00.00.00.00.00.07960.09649.08518.08628.09293.05033.02000.01000.0500.01500.00.02500.00.02